18. Markov Assumption for Motion Model: Explanation
Explain Markov Assumption For Motion Model
Markov Assumption
A Markov process is one in which the conditional probability distribution of future states (ie the next state) is dependent only upon
the current state and not on other preceding states. This can be expressed mathematically as:
P(x_t|x_{1-t},....,x_{t-i},...., x_0) = P(x_t|x_{t-1})
It is important to note that the current state may contain all information from preceding states. That is the case discussed in this lesson.